Field
The disclosed concept pertains generally to hybrid power systems and, more particularly, to apparatus for optimizing such hybrid power systems. The disclosed concept further pertains to methods for optimizing hybrid power systems.
Background Information
Buildings are responsible for over 70% of the U.S. electricity consumption, 40% of the total U.S. energy consumption, and an equivalent fraction of carbon emissions. However, the development and deployment of energy efficient technology in buildings lags behind that of the transportation and industrial sectors. The reasons for this discrepancy include the wide diversity of energy-consuming and energy-saving technologies in buildings. The energy consumption of buildings involves a wide range of different technologies. Space conditioning systems (e.g., HVAC) deliver heating, cooling, and air circulation/cleansing. Lighting systems deliver illumination; water heating and sanitation systems deliver and dispose of water; electrical and gas systems deliver power and fuel; elevators and escalators provide mobility; and integrated renewable systems generate power.
Since fossil energy resources are gradually depleted and are an increasingly serious issue of environmental pollution, it has become the consensus of most countries in the world to develop renewable energy represented by wind energy and solar energy for the sustainable development of human society. Both solar-based and wind-based energy are effective after decades of development, but neither are free of issues. The most notable issue is the possible lack of wind or sun, which will prevent power generation. The best solution to this problem is to create a hybrid power system, which is a combination of two or more different power sources. Combining two or more power sources will make for a much more steady production of power, with less potential for outages in case one source of power is lacking. In order to draw the best performance of such systems, proper energy management is essential.
Hybrid power source management should first ensure continuous power supply to the load. Thereafter, other targets may be set (e.g., fault tolerance of an element; maximizing efficiency; reducing operating cost). Hybrid power sources are also used beyond residential/commercial building areas. They have been successfully used to power hybrid electric vehicles using selected combinations of internal combustion engines, fuel cells, batteries and super-capacitors. They are further deployed in all-electric ships to build a distributed shipboard electric power system. They can also be used in a bulk power system to construct an autonomous distributed energy unit.
A suitable control strategy takes advantage of inherent scalability and robustness benefits of the hybrid power system. Traditionally, heuristic control strategies are used in energy management. The control strategies are based on an “if-then-else” type of control rules, which determine, for example, which power source is employed. Fuzzy logic algorithms are known to determine fuel cell output power depending on external power requirements and battery state-of-charge. Since heuristic control strategies do not require models of systems, they are relatively easy to design and implement.
In most buildings, diverse loads operate largely independently. For instance, space conditioning systems, lacking coordinated controls, can simultaneously heat and cool building air, dramatically increasing energy use. Enhancing the integration of these diverse systems, expanding their coordinated operation through distributed sensor and control networks, and ensuring they are maintained in optimal working condition, can lead to important efficiency gains. Load management could be introduced to prevent conflicting simultaneous operation of heating and cooling systems and unnecessary space conditioning, lighting and mobility services. Hence, intelligent load management should also be included in the control strategies of hybrid power systems.
Modeling of a hybrid power system is needed to manage both power sources and different loads to achieve maximum efficiency of an entire building. A known probabilistic model allows estimation of the long-term average performance of a hybrid solar-wind power system. A closed form solution approach can be employed to convolute the wind energy and the photovoltaic system. For short term performance, only a deterministic formulation can be used. Since the hybrid power system consists of different power sources and loads, it is intuitive to treat the system as a network. A multi-agent technology has been successfully applied in manufacturing, transportation, and many other fields, and can also be applied to manage power sharing between multiple sources and loads in a hybrid power system. In a multi-agent based hybrid power system, each energy source and load is represented as an autonomous agent that provides a common communication interface for all different components. With this structure, distributed control, with decision-making done locally within each power source and load, can facilitate coordination of the agents and potentially create a scalable and robust hybrid power system. If an agent goes off-line, other agents are able to cope with the loss of that agent and re-organize the system.
Although multi-agent system modeling has many advantages, centralized management, which is also known as a “top-down” approach, seems preferable for many applications. The reason is there are relatively mature control/optimization theories available for centralized-based decision making. Also, centralized decision making is usually more efficient as compared with a de-centralized counterpart, and it results in relatively simple rules established according to the constraints and objectives. A hierarchical system control divides the decision-making process into different levels, in which only some of them in a straight line access the control system. The decision-maker units that define tasks and coordinate are at a higher level of the hierarchy, while the lower levels have direct contact with the process. For a hybrid power system, the energy management unit could be treated as a relatively higher level decision-maker, and the control systems that regulate the voltage and current of the system are then treated as lower level units. The presence of switching modes and the constraints of power sources and loads make the problem inherently have continuous and discrete dynamic behavior, which can be modeled and controlled under hybrid control theory.
A hybrid model of a dynamic system describes the interaction between continuous dynamics described by differential equations, and logical components described by finite state machines, IF-THEN-ELSE rules, and propositional and temporal logic. Several classes of hybrid systems have been proposed, such as Discrete Hybrid Automata (DHA), Mixed Logical Dynamical (MLD) models, Piecewise Linear (PWA) systems, and max-min-plus-scaling (MMPS) systems. However, it is believed that all of those modeling frameworks are equivalent under some hypothesis and it is possible to represent the same system with different models.
In a hybrid power system, the term “hybrid” means the combination of different power sources and loads. In hybrid control theory, the term “hybrid” means the combination of continuous dynamics and logic components.
Based on the hybrid model of control, reachability analysis and piecewise quadratic Lyapunov stability are standard tools for hybrid system analysis. Reachability analysis, or safety analysis or formal verification, aims at detecting if a hybrid model will eventually reach an unsafe state configuration or satisfy a temporal logical formula. Reachability analysis relies on a reach set computation algorithm, which is strongly related to the mathematical model. Piecewise quadratic Lyapunov stability is often used to prove the stability of the hybrid system. The computational burden is usually low but it produces conservative results due to the convex relaxation of the problem.
In addition to the mathematical modeling of a hybrid system, system identification techniques for piecewise affine (i.e., can be described by a type of format: “ax+b”) systems are also known that allow derivation of models from input-output data.
Different methods for the analysis and design of controllers for hybrid systems are known. The approaches differ greatly in the hybrid models adopted, in the formulation of the optimal control problem and in the method used to solve it. The state-feedback optimal control law can be constructed by combining multi-parametric programming and dynamic programming. A model predictive control scheme is known to stabilize Mixed Logical Dynamical (MLD) systems on desired reference trajectories while fulfilling operating constraints. Similarly, the dual problem of state estimation is known to admit a receding horizon solution scheme.
A micro-grid energy management system is a supervisory control system to manage power flow to optimize the operation of a micro-grid, for example, by minimizing the fuel consumption of generators. The nature of a micro-grid makes traditional numerical optimization techniques, such as linear programming and nonlinear programming, not applicable. The traditional optimization method is based on continuous functions, such as differential equations. However, a micro-grid, by nature, has many “discrete” modes or components, such as the modes of operation of generators, and switches. In reality, many approximations have to be made to use numerical optimization techniques. Of course, those will lose optimality during approximation.
Known micro-grid long-term power management is based on an “if-then-else” type heuristic control strategy. However, a heuristic control strategy is an experience-based method and, as a result, the maximum efficiency is not guaranteed.
There is room for improvement in apparatus for optimizing hybrid power systems.
There is also room for improvement in methods for optimizing hybrid power systems.